Bibliographic record
Abstract
In 2008, a condition survey indicated that major structural improvements were necessary to extend the service life of the 50-year-old United Harvest grain import/export dock located on the Columbia River at the Port of Kalama in Kalama, Washington. Rather than repair the dock, the owner elected to replace it, modernize the dockside grain-handling equipment, and install two 150-foot [45.7 m] boom fixed-tower ship loaders to increase the ship loading rate from 1,200 metric tons per hour (mtph) [1,323 short tons per hour (stph)] to 3,200 mtph [3,527 stph]. To support the towers and resist the ship berthing and mooring loads, two new batter pile-supported concrete platforms were constructed. Because the riverbed at the site consisted of approximately 65 feet [20 m] of highly liquefiable sand over solid basalt, a micropile was installed inside each batter pile and pretensioned into the basalt to resist the high-tension loads on the piles. By setting the lock-off load equal to just greater than the maximum non-seismic tensile demand of each pile, the micropile force/displacement response exhibited a bilinear behavior under tensile loads. The tensile strain of the pile/micropile system increases when the non-seismic forces are overcome, and this behavior is used to reduce seismic forces on the platform. This paper focuses on the selection of the fixed-tower ship loaders, the design of the ship loader platforms, and the pile/micropile bilinear response and its incorporation into the design per the International Building Code (IBC).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".